@article{M8F5EF4E2,
title = "Grasping a Target Object in Clutter with an Anthropomorphic Robot Hand via RGB-D Vision Intelligence, 
Target Path Planning and Deep Reinforcement Learning",
journal = "KIPS Transactions on Software and Data Engineering",
year = "2022",
issn = "2287-5905",
doi = "https://doi.org/10.3745/KTSDE.2022.11.9.363",
author = "Ga Hyeon Ryu/Ji-Heon Oh/Jin Gyun Jeong/Hwanseok Jung/
Jin Hyuk Lee/Patricio Rivera Lopez/Tae-Seong Kim",
keywords = "Anthropomorphic Robot Hand, Reinforcement Learning, Path Planning, Object Detection",
abstract = "Grasping a target object among clutter objects without collision requires machine intelligence. Machine intelligence includes environment 
recognition, target & obstacle recognition, collision-free path planning, and object grasping intelligence of robot hands. In this work, 
we implement such system in simulation and hardware to grasp a target object without collision. We use a RGB-D image sensor to recognize 
the environment and objects. Various path-finding algorithms been implemented and tested to find collision-free paths. Finally for an 
anthropomorphic robot hand, object grasping intelligence is learned through deep reinforcement learning. In our simulation environment, 
grasping a target out of five clutter objects, showed an average success rate of 78.8%and a collision rate of 34% without path planning. 
Whereas our system combined with path planning showed an average success rate of 94% and an average collision rate of 20%. In our 
hardware environment grasping a target out of three clutter objects showed an average success rate of 30% and a collision rate of 97% 
without path planning whereas our system combined with path planning showed an average success rate of 90% and an average collision 
rate of 23%. Our results show that grasping a target object in clutter is feasible with vision intelligence, path planning, and deep RL."
}